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Reversing age by reprogramming gene expression networks

Every organ and tissue progressively loses function and integrity as it ages. In fact, researchers have identified “hallmarks of aging” that not only present specific processes impacted by age, but are also thought of as levers that, if successfully targeted, could help turn back the aging clock and result in tissue rejuvenation.

In 2006, Shinya Yamanaka’s Nobel Prize–winning discovery that the overexpression of four genes, the “Yamanaka factors,” can reprogram most mature cell types back to an undifferentiated, pluripotent ground state was a milestone in aging research. Interestingly, when expressed only in short bursts, the Yamanaka factors can also partially reprogram old cells back to a more youthful state, reversing multiple hallmarks of aging while maintaining their specialized functions. This and other insights showed that aging does not need to be a one-way street and that aging cells retain a latent capacity for rejuvenation.

Unfortunately, the therapeutic use of Yamanaka factors is causing concern for some researchers because their overexpression also raises the risk of cancer formation. Researchers have therefore been hunting for other factors for rejuvenating aging tissues in safer and more controllable ways. However, “in reprogramming, there is a fundamental trade-off between rejuvenation and cell identity; achieving one without losing the other requires walking a fine line,” said Alex Plesa, Ph.D., the co-first author on a study presenting a new potential avenue to solving this challenge.

Reprogrammed NK immune cells target cancer and autoimmune disorders

MIT engineers have developed a new way to program natural killer (NK) immune cells to target cancerous T cells or T cells that have become autoreactive, leading to diseases such as lymphoma and autoimmune hepatitis.

Using their new technique, the researchers can reprogram NK cells to target diseased T cells without ever removing the NK cells from the patient’s body. Instead, they use lipid nanoparticles to deliver messenger RNA directly to NK cells. These mRNA molecules encode proteins that allow the NK cells to seek and destroy the dysfunctional T cells.

Known as CAR NK cells, these cells could be deployed against a variety of diseases. The MIT team’s new approach could bypass one of the major obstacles to using these cells, which is the time and expense of manufacturing them outside the body.

Einstein was right: Darkness can travel faster than light without breaking relativity

Dark points within a changing wave pattern can shift faster than light, even while the waves carrying energy move far more slowly. An electron microscopy experiment has tracked that counterintuitive behavior in a thin material sample.

The moving features are locations where a measured wave field cancels to zero. Their motion describes an evolving pattern, so it does not provide a way to send information faster than light.

The study, published in Nature, was led by researchers at the Technion-Israel Institute of Technology. Tomer Bucher and Alexey Gorlach worked with Ido Kaminer’s team and an international collaboration to map these optical phase singularities.

Quantum Is Coming Faster Than Many Organizations Are Preparing For. Waiting Is a Risk We Cannot Afford

Please see my article in the latest issue of the Security & Tech Insights newsletter. A significant gap exists between the rate of quantum innovation and many organizations’ readiness to manage its consequences. Businesses, healthcare systems, financial institutions, government agencies, and critical infrastructure operators continue to rely on cryptographic systems designed for a world where certain mathematical problems were effectively beyond the capabilities of traditional computers. Advances in quantum computing may eventually change this assumption.

The danger is not that every existing encryption system will be rendered vulnerable tomorrow (although it is possible).

A Major Superconductor ‘Speed Limit’ Was Just Broken

Superconductors give us sci-fi-worthy technologies like levitating trains, Big-Bang-replicating particle colliders, and medical machines that reveal our innermost secrets – thankfully only the physiological ones and not, perhaps, a secret love of polka dance.

But superconducting states are fragile. Materials must be cooled to very low temperatures, approaching the dark frigidity of outer space, to conduct electricity without resistance and subsequent energy loss.

The strength of the electrical current and of external magnetic fields can also snap these materials out of their superconducting states, as a curious cat’s whisker or mistimed sneeze can knock down a precarious series of dominos.

Comprehensive Approach to Prostate Cancer Metastasis Mimics at Prostate-Specific Membrane Antigen PET/CT

Prostate-specific membrane antigen (PSMA) PET has transformed prostate cancer (PCa) imaging. Nevertheless, major diagnostic pitfalls remain as PSMA expression is not exclusive to PCa and may also occur in physiologic tissues and a range of benign and malignant conditions. These conditions may mimic PCa metastases, leading to potential misinterpretation. Accurate risk assessment of suspected lesions using PSMA PET/CT requires the integration of uptake intensity, lesion location, corresponding anatomic imaging findings, and clinical context. The authors outline common and diagnostically challenging mimics of PCa metastases seen at PSMA PET/CT and present a structured risk-assessment approach that incorporates current standardized scoring systems.

©RSNA, 2026

Supplemental material is available for this article.

AI can now clone any game in hours!

As I continue down the rabbit hole of AI I realize that it goes deeper and deeper. I dug into the decomp and recomp aspect of it, to figure out how games are being cloned and how they are mashing up two different AAA games.

That means for indies, that their games are in danger of being cloned from the moment we release a demo.

#gamedev #makinggames #genai

Mitigating AI-Enabled Compromise, Insider Risk Threats and the Human Attack Surface

Cybersecurity has entered an era in which the distinction between an external attack and an internal compromise is becoming increasingly difficult to maintain. For years, organizations built security programs around a relatively straightforward model: defend the perimeter, protect the network, secure endpoints, educate employees, and respond when an attacker gets through. That model was already under pressure from cloud computing, remote work, mobile devices, software supply chains, and the Internet of Things. Artificial intelligence is now accelerating that transformation and changing the economics of both offense and defense.

The emerging threat is not simply an AI-powered hacker breaking through a firewall. It is a much more complicated ecosystem in which artificial intelligence can observe, personalize, automate, and accelerate attacks against identities, employees, applications, vendors, and business processes. An employee has a certain level of authority. An application has another. An autonomous AI agent could potentially combine access to multiple systems and perform a sequence of actions much faster than a human. An AI assistant that can read a database is one thing. An AI agent that can read the database, modify records, send emails, access financial systems, communicate with external services, and initiate transactions is entirely different.

The principle of least privilege therefore needs to extend to machines. AI agents should have only the permissions necessary to perform their assigned functions. Sensitive information should be segmented. High-impact actions should require appropriate human approval. Agent activity should be logged and monitored, and organizations should maintain the ability to revoke access quickly.

Pure consciousness reveals a boundless reality beyond objects

When we are conscious, we are conscious of some thing: be it an object, a person, or an idea. But China-based philosopher Itay Shani argues that such “ordinary consciousness” is made possible by what he calls “pure consciousness”—a consciousness without objects, structure, boundaries, or form, which meditative and mystical traditions have long been aware of. Pure consciousness, Shani contends, is awareness of a boundless reality that no word or thought could ever capture, that can only be gestured at using negation, metaphor and contradiction. To recognize it is to recognize that there are worlds beyond our familiar world of objects and things.

When contemporary scientists and philosophers contemplate the question “What is consciousness?” they tend to do so by focusing on phenomenal states, namely, mental states endowed with a qualitative feel — a “what it’s like” character. When one is awake, aware, and fully in one’s senses, one is typically subject to a continuous stream of experiences involving rapidly unfolding trains of thoughts, feelings, and images, and accompanied by longer-lasting moods and emotional overtones.

Such phenomenal states feel a certain way. The sweet smell of the rose; the icy chill of a morning swim in the river; the tender pangs of longing — all are different from each other in terms of what it is like to have them. They are also, generally, directed at certain objects of thought, real or imaginary. A conscious experience is therefore said to be “of” or “about” something. There is something we think of when we think, something we perceive when we perceive, and so on. Moreover, these objects are presented in consciousness from a certain point of view—the perspective of the conscious subject. For example, objects are viewed from a particular angle, perceived as approaching or receding, as far or near, as surprising or familiar, and so on.

Leveraging traditional and advanced machine learning for comprehensive oncology data integration and outcome prediction

Outcome prediction in neuro-oncology remains challenging due to heterogeneity in tumor biology, imaging characteristics, treatment response, and patient-specific clinical factors. Although molecular databases have advanced understanding of brain tumor biology, many datasets lack broader data necessary for generalizable predictive modeling. Artificial intelligence (AI) and machine learning (ML) are increasingly being explored to integrate these heterogeneous data sources and support precision neuro-oncology.

A narrative review was performed using PubMed and Embase to identify studies evaluating oncology databases, ML models, multimodal data integration, and AI applications relevant to neuro-oncology. Studies published between 2020 and 2026 were preferentially reviewed, with additional landmark studies included when relevant.

Traditional ML approaches including regression-based models, random forests, support vector machines, and gradient boosting algorithms have been applied to survival prediction, recurrence classification, treatment response estimation, and molecular biomarker prediction using clinical, imaging, molecular, and population-level datasets. More advanced architectures including natural language processing, large language models, vision transformers, geometric deep learning, and graph neural networks have expanded the ability to analyze unstructured and multimodal data sources including radiology reports, histopathology slides, MRI volumes, molecular datasets, and electronic health records. Multimodal AI systems integrating imaging, molecular, histopathologic, clinical, and socioeconomic data frequently demonstrated improved predictive performance compared with single-modality models.